讲座内容：Uncertainty and bias in Machine learning: New Direction in Evidential Deep Learning（机器学习中的不确定性和偏差：基于证据的深度学习新方向）
讲座人：Hamido Fujita教授
讲座时间：11月8日 14:00-15:30
腾讯会议：466-514-555
Abstract：
In the current field of artificial intelligence, deep learning has achieved significant results in various tasks. However, most deep learning methods still face problems of uncertainty and instability, which leads to insufficient generalization ability of the model when facing unknown data. In order to solve this problem, Evidence Deep Learning (EDL) has emerged. The core idea of EDL is to introduce evidence theory into deep learning frameworks to achieve consistent modeling of model output uncertainty. By introducing evidence synthesis operations in neural networks, EDL can capture the model's confidence in different categories, thereby improving the model's generalization ability when facing unknown data. Meanwhile, EDL can effectively balance overfitting and underfitting phenomena, improving the performance of the model in complex scenarios.
Short Bio：
He is Executive Chairman of i-SOMET Incorporated Association, Japan, and Distinguished Professor at Iwate Prefectural University, Japan, he is also Research Professor at University of Granada, Spain. He is Highly Cited Researcher in CrossField for the year 2019 and in Computer Science for the year 2020, 2021 and 2022, by Clarivate Analytics. He received Doctor Honoris Causa from Óbuda University, Budapest, Hungary, in 2013 and received Doctor Honoris Causa from Timisoara Technical University, Timisoara, Romania, in 2018, and a title of Honorary Professor from Óbuda University, in 2011. He is Distinguished Research Professor at the University of Granada, and Adjunct Professor with Taipei Technical University, Taiwan, Harbin Engineering University, China and others. He supervised Ph.D. students jointly with the University of Laval, Quebec City, QC, Canada; University of Technology Sydney; Oregon State University, Corvallis, OR, USA; University of Paris 1 Pantheon-Sorbonne, Paris, France; and University of Genoa, Italy. Dr. Fujita is the recipient of the Honorary Scholar Award from the University of Technology Sydney, in 2012. He was the Editor-in-Chief for Knowledge-Based Systems (Elsevier) (2005- 2019) and then Emeritus Editor of Knowledge-Based Systems in 2020~. Since 2020 he is currently the Editor-in-Chief of Applied Intelligence (Springer) and the Editor-inChief of International Journal of Healthcare Management (Taylor & Francis). He headed a number of projects including intelligent HCI, a project related to mental cloning for healthcare systems as an intelligent user interface between human-users and computers, and SCOPE project on virtual doctor systems for medical applications. He collaborated with several research projects in Europe, and recently he is collaborating in OLIMPIA project supported by Tuscany region on Therapeutic monitoring of Parkison disease. He has published more than 400 articles mainly in high impact factor journals.
